---
title: "Insights — Putting the Analyses Together"
---
The eight preceding analysis chapters each answer a different question. The interesting work is what they say *jointly* — where they agree, where they disagree, and what the synthesis recommends.
This chapter joins the per-chapter outputs on a common key (product name on the article side, customer id on the customer side) and surfaces the cross-cuts that are most actionable. **For the headline numbers and visual snapshots use the [dashboard](dashboard.html); this page is the prose narrative.**
| Source chapter | Question it answers | Used here for |
|---|---|---|
| 01 Association rules | Which products co-occur in baskets? | Cross-sell pair recommendations |
| 02 BCG clustering | Which products dominate by share × growth? | Portfolio tier classification |
| 03 RFM clustering | Which products are healthy / dying by R/F/M? | Portfolio tier classification |
| 04 CLV (BG/NBD) | Per-customer value forecast | Customer-tier classification |
| 06 Survival | When does the comeback rate decay? | Time-windowed retention triggers |
| 07 Forecasting | What's the next-3-months revenue per category? | Inventory and budget planning |
| 08 Embeddings | Which products are functionally similar? | Substitution lookup |
| 09 Causal uplift | Did the discount *cause* the repeat? | Targeting with positive uplift |
```{python}
#| label: setup
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules
from lifetimes import BetaGeoFitter, GammaGammaFitter
from lifetimes.utils import summary_data_from_transaction_data
sns.set_theme(style="whitegrid")
RANDOM_STATE = 42
from pathlib import Path
_data_path = "data/raw/transactions.csv" if Path("data/raw/transactions.csv").exists() else "data/synthetic/transactions.csv"
df = pd.read_csv(_data_path, sep=";", parse_dates=["date"])
REF_DATE = df["date"].max()
SPLIT = df["date"].min() + (REF_DATE - df["date"].min()) / 2
```
## Building one product table from BCG + RFM
Each chapter scored products on different dimensions. We now compute both side-by-side and merge:
```{python}
#| label: product-table
# BCG: share × growth
df["period"] = np.where(df["date"] <= SPLIT, "p1", "p2")
period_rev = (
df.groupby(["article_name", "period"])["gross_price"].sum()
.unstack(fill_value=0.0)
.rename(columns={"p1": "rev_p1", "p2": "rev_p2"})
)
period_rev["revenue"] = period_rev["rev_p1"] + period_rev["rev_p2"]
period_rev["share"] = period_rev["revenue"] / period_rev["revenue"].sum()
period_rev["growth"] = np.where(
period_rev["rev_p1"] > 0,
(period_rev["rev_p2"] - period_rev["rev_p1"]) / period_rev["rev_p1"],
np.where(period_rev["rev_p2"] > 0, 1.0, -1.0),
)
X_bcg = StandardScaler().fit_transform(period_rev[["growth", "share"]])
km_bcg = KMeans(n_clusters=4, random_state=RANDOM_STATE, n_init=10).fit(X_bcg)
period_rev["bcg_cluster"] = km_bcg.labels_
centers = pd.DataFrame(km_bcg.cluster_centers_, columns=["growth_z", "share_z"])
centers["weight"] = 0.5 * centers["growth_z"] + 0.5 * centers["share_z"]
centers["bcg_rank"] = centers["weight"].rank(ascending=False).astype(int)
period_rev["bcg_rank"] = period_rev["bcg_cluster"].map(centers["bcg_rank"])
# RFM at article level
rfm = (
df.groupby("article_name")
.agg(frequency=("article_name", "count"),
value=("gross_price", "sum"),
last_sold=("date", "max"))
)
rfm["recency"] = (REF_DATE - rfm["last_sold"]).dt.days / 30.4375
X_rfm = StandardScaler().fit_transform(rfm[["recency", "frequency", "value"]])
km_rfm = KMeans(n_clusters=4, random_state=RANDOM_STATE, n_init=10).fit(X_rfm)
rfm["rfm_cluster"] = km_rfm.labels_
rcen = pd.DataFrame(km_rfm.cluster_centers_, columns=["rec_z", "freq_z", "val_z"])
rcen["score"] = (rcen["freq_z"] + rcen["val_z"] - rcen["rec_z"]) / 3
rcen["rfm_rank"] = rcen["score"].rank(ascending=False).astype(int)
rfm["rfm_rank"] = rfm["rfm_cluster"].map(rcen["rfm_rank"])
products = period_rev[["share", "growth", "bcg_rank"]].join(
rfm[["recency", "frequency", "value", "rfm_rank"]]
)
products.head()
```
### Where BCG and RFM agree
```{python}
#| label: fig-bcg-rfm-overlap
#| fig-cap: "Cross-tab of BCG rank × RFM rank. The diagonal is where the two methods agree; off-diagonal cells are products where one method sees them more favorably than the other."
xtab = pd.crosstab(products["bcg_rank"], products["rfm_rank"], margins=True, margins_name="Total")
fig, ax = plt.subplots(figsize=(6.5, 4.5))
sns.heatmap(
xtab.iloc[:-1, :-1],
annot=True, fmt="d", cmap="Blues", cbar=False, ax=ax,
)
ax.set_xlabel("RFM rank (1 = healthy core)")
ax.set_ylabel("BCG rank (1 = star)")
plt.tight_layout()
plt.show()
```
```{python}
#| label: agreement
both_top = products[(products["bcg_rank"] == 1) & (products["rfm_rank"] == 1)].index.tolist()
both_bottom = products[(products["bcg_rank"] == 4) & (products["rfm_rank"] == 4)].index.tolist()
disagree = products[abs(products["bcg_rank"] - products["rfm_rank"]) >= 2].index.tolist()
print(f"Both top (BCG=1 AND RFM=1): {both_top}")
print(f"Both bottom (BCG=4 AND RFM=4): {both_bottom}")
print(f"Disagree by ≥ 2 ranks: {disagree}")
```
The diagonal cluster shows products that are robust across both lenses — these are the safest products to invest in. Off-diagonal cells flag products where the two methods *see different things*: typically these are products in transition (fast-growing but small share, or shrinking but still high-frequency).
## Co-purchase rules — which apply to *top* products?
We use only the **actionable** rules from chapter 01 here — the ones that survived the bundle / symmetry triage. Definitional pairs (`bed ↔ mattress`, `headboard ↔ bed`) are filtered out so they don't pollute the cross-sell list.
```{python}
#| label: rules-top
#| message: false
# Drop unmapped/empty article_names — mixing NaN with strings crashes apriori's sort.
df_rules = df[df["article_name"].notna() & (df["article_name"].astype(str).str.strip() != "")].copy()
df_rules["article_name"] = df_rules["article_name"].astype(str).str.strip()
baskets = df_rules.groupby("transaction_id")["article_name"].apply(lambda s: list(set(s))).tolist()
te = TransactionEncoder()
basket_matrix = pd.DataFrame(te.fit_transform(baskets), columns=te.columns_)
freq_items = apriori(basket_matrix, min_support=0.001, use_colnames=True)
rules = association_rules(freq_items, num_itemsets=len(basket_matrix),
metric="confidence", min_threshold=0.5)
simple = rules[
(rules["antecedents"].apply(len) == 1) & (rules["consequents"].apply(len) == 1)
].copy()
simple["antecedent"] = simple["antecedents"].apply(lambda s: next(iter(s)))
simple["consequent"] = simple["consequents"].apply(lambda s: next(iter(s)))
# Apply triage — same logic as chapter 01 (accessory + symmetry from raw baskets)
ACCESSORY_ITEMS = {"headboard", "nightstand", "table_extension"}
bundle_lookup = (
df.drop_duplicates("article_name").set_index("article_name")["bundle_group"]
.fillna("").to_dict()
)
basket_sets_05 = df.groupby("transaction_id")["article_name"].apply(set).tolist()
def cond_prob_05(a, b):
n_b = sum(1 for s in basket_sets_05 if b in s)
if n_b == 0:
return 0.0
n_both = sum(1 for s in basket_sets_05 if a in s and b in s)
return n_both / n_b
simple["reverse_conf"] = simple.apply(
lambda r: cond_prob_05(r["antecedent"], r["consequent"]), axis=1
)
simple["symmetry"] = simple.apply(
lambda r: min(r["confidence"], r["reverse_conf"]) / max(r["confidence"], r["reverse_conf"])
if max(r["confidence"], r["reverse_conf"]) > 0 else 0.0,
axis=1,
)
simple["within_bundle"] = simple.apply(
lambda r: bundle_lookup.get(r["antecedent"], "") == bundle_lookup.get(r["consequent"], "")
and bundle_lookup.get(r["antecedent"], "") != "",
axis=1,
)
simple["accessory_to_primary"] = (
simple["antecedent"].isin(ACCESSORY_ITEMS) & simple["within_bundle"]
)
simple = simple[~simple["accessory_to_primary"] & (simple["symmetry"] < 0.7)].copy()
# Annotate with BCG / RFM rank of the consequent
simple["consequent_bcg"] = simple["consequent"].map(products["bcg_rank"])
simple["consequent_rfm"] = simple["consequent"].map(products["rfm_rank"])
# Rules where the consequent is a top product (BCG=1 OR RFM=1)
top_rules = (
simple[(simple["consequent_bcg"] == 1) | (simple["consequent_rfm"] == 1)]
.sort_values("confidence", ascending=False)
)
top_rules[["antecedent", "consequent", "support", "confidence", "lift",
"consequent_bcg", "consequent_rfm"]].head(8).round(3)
```
Rules that *push customers toward* top-tier products are the highest-leverage ones for cross-sell prompts: customers buying the antecedent are 5–10× more likely than chance to buy the (already-strong) consequent. Easy upsell.
## Customer concentration — quantifying the Pareto
```{python}
#| label: clv-pareto
tx = df.groupby(["customer_id", "transaction_id", "date"], as_index=False)["gross_price"].sum()
summary = summary_data_from_transaction_data(
tx, "customer_id", "date", monetary_value_col="gross_price",
observation_period_end=tx["date"].max(), freq="D",
)
bgf = BetaGeoFitter(penalizer_coef=0.001).fit(summary["frequency"], summary["recency"], summary["T"])
returning_only = summary[(summary["frequency"] >= 1) & (summary["monetary_value"] > 0)].copy()
ggf = GammaGammaFitter(penalizer_coef=0.01).fit(returning_only["frequency"], returning_only["monetary_value"])
returning_only["clv_12m"] = ggf.customer_lifetime_value(
bgf, returning_only["frequency"], returning_only["recency"], returning_only["T"],
returning_only["monetary_value"], time=12, discount_rate=0.01, freq="D",
)
returning_sorted = returning_only.sort_values("clv_12m", ascending=False)
total = returning_sorted["clv_12m"].sum()
for q in [0.10, 0.20, 0.30, 0.50]:
cum = returning_sorted["clv_12m"].iloc[: int(q * len(returning_sorted))].sum()
print(f" top {int(q*100):>2d}% of returning customers → {cum / total:.1%} of forecast 12-month revenue")
```
The top 20% of returning customers cover roughly half of forecast revenue — a clean Pareto. *(See the [dashboard](dashboard.html) for the Lorenz curve.)*
## Sizing the recommendations
Before turning to recommendations, pin down some concrete numbers. Each finding below is annotated with what it *would mean* in EUR or counts.
```{python}
#| label: sizing
# --- Product side: revenue concentration ---
total_revenue = products["value"].sum()
top10_products = products.nlargest(10, "value")
top10_share = top10_products["value"].sum() / total_revenue
bottom_overlap = products[(products["bcg_rank"] == 4) & (products["rfm_rank"] == 4)]
bottom_revenue_share = bottom_overlap["value"].sum() / total_revenue
# --- Top association rule: missed cross-sell sizing ---
top_rule = simple.iloc[0]
ant, con = top_rule["antecedent"], top_rule["consequent"]
basket_sets = df.groupby("transaction_id")["article_name"].apply(set)
ant_baskets = basket_sets[basket_sets.apply(lambda s: ant in s)]
ant_n = len(ant_baskets)
con_in_ant = ant_baskets.apply(lambda s: con in s).sum()
miss_n = ant_n - con_in_ant
avg_con_price = df.loc[df["article_name"] == con, "gross_price"].mean()
miss_eur = miss_n * avg_con_price
# --- Customer side: CLV decile sizing ---
n_returning = len(returning_sorted)
total_clv_12m = returning_sorted["clv_12m"].sum()
top10pct_n = int(0.10 * n_returning)
top10pct_clv = returning_sorted.head(top10pct_n)["clv_12m"].sum()
top20pct_clv = returning_sorted.head(2 * top10pct_n)["clv_12m"].sum()
# --- Win-back zone customers ---
clv_summary_with_alive = summary.copy()
clv_summary_with_alive["p_alive"] = bgf.conditional_probability_alive(
summary["frequency"], summary["recency"], summary["T"]
)
winback_zone = clv_summary_with_alive[
(clv_summary_with_alive["p_alive"].between(0.4, 0.7))
& (clv_summary_with_alive["frequency"] >= 1)
]
# --- Discount spend ---
total_discount_eur = float(df["discount_amount"].sum())
discounted_lines = int((df["discount_amount"] > 0).sum())
total_lines = len(df)
# --- Survival drops ---
first_dates = df.groupby("customer_id")["date"].min()
second_dates = (
df.sort_values("date").groupby("customer_id")["date"].nth(1)
)
print("=" * 60)
print("PRODUCT SIDE")
print("=" * 60)
print(f"Total catalog revenue: €{total_revenue:>12,.0f}")
print(f"Top 10 products: €{top10_products['value'].sum():>12,.0f} ({top10_share:.0%} of total)")
print(f" the top 10: {', '.join(top10_products.index.tolist())}")
print()
print(f"BCG=4 ∩ RFM=4 (delisting candidates): {len(bottom_overlap)} products, €{bottom_overlap['value'].sum():,.0f} ({bottom_revenue_share:.1%} of total)")
print(f" members: {', '.join(bottom_overlap.index.tolist()) or '(none)'}")
print()
print(f"Top rule: {ant} → {con}")
print(f" {ant_n} baskets contained {ant}; {con_in_ant} ({con_in_ant/ant_n:.0%}) also bought {con}")
print(f" miss: {miss_n} baskets had {ant} but no {con}; upsell value @ avg €{avg_con_price:,.0f}/{con}: €{miss_eur:,.0f}")
print()
print("=" * 60)
print("CUSTOMER SIDE")
print("=" * 60)
print(f"Returning customers: {n_returning}")
print(f"Total forecast 12-month CLV: €{total_clv_12m:>12,.0f}")
print(f" top 10% ({top10pct_n} customers): €{top10pct_clv:,.0f} ({top10pct_clv/total_clv_12m:.0%})")
print(f" top 20% ({2*top10pct_n} customers): €{top20pct_clv:,.0f} ({top20pct_clv/total_clv_12m:.0%})")
print()
print(f"Win-back zone (P(alive) ∈ [0.4, 0.7], ≥1 prior purchase): {len(winback_zone)} customers")
print()
print(f"Total discounts given: €{total_discount_eur:,.0f} ({discounted_lines}/{total_lines} = {discounted_lines/total_lines:.0%} of line items)")
print(f" measured causal lift on repurchase: 0% (95% CI straddles zero)")
print(f" → at face value: €{total_discount_eur:,.0f} of revenue forgone with no detectable retention payoff in this synthetic dataset")
```
## Granularity choices per analysis
The catalog has a 5-level hierarchy (Department → Category → Family → Model → SKU). Different methods use different levels. The choice isn't free — pick wrong and you either overfit (too fine) or lose signal (too coarse). Here's what each chapter uses and why:
| Chapter | Level | Why this level |
|---|---|---|
| 01 Association rules | Family (`article_name`) | Customers care about *what* not *which model*. `bed → mattress` is a more useful rule than `harmony bed → premium mattress`. |
| 02 BCG clustering | Family | 40 names is the sweet spot for a 4-cluster k-means. Going to SKU (66) just adds variance, going to Category (10) leaves too few points. |
| 03 RFM clustering | Family | Same reasoning as BCG. |
| 04 CLV (BG/NBD) | Customer | The level above all of these — but the *unit of value* is the basket euro, which doesn't depend on product granularity. |
| 06 Survival | Customer + Family covariates | Customer-level outcome; basket-feature covariates use Family. |
| 07 Forecasting | Both Department and Category | Department for executive view (6 series, more signal), Category for operational planning (10 series, finer detail). |
| 08 Embeddings | **SKU** (`article_id`) | Substitution lookup — the operational use case — needs SKU granularity to answer "which `bed` model is closest to the out-of-stock one". Family level would collapse all sofa SKUs to one point. |
| 09 Causal uplift | Customer + Family covariates | Same logic as Survival. |
The common pattern: **start at Family, only step up or down when there's a specific reason**. Step up to Department when individual categories are too sparse (Forecasting on Outdoor / Storage). Step down to SKU only when SKU-level decisions are at stake (inventory, replenishment).
## Triage — what's actually insight, what's plumbing?
Before recommendations, classify every finding the chapters surfaced. This separates *insights you should act on* from *sanity checks* (the analysis worked, but you knew this) and *data-quality artifacts* (something to fix in the catalog, not in marketing).
| # | Finding | Class | Why this class |
|---|---|---|---|
| 1 | Heavy revenue concentration in a small head of the catalog | 🟢 actionable insight | Concrete sizing for portfolio prioritization (numbers in the *sizing* block) |
| 2 | Asymmetric cross-bundle co-purchase pairs (e.g. table → chair) | 🟢 actionable insight | Real cross-sell levers — antecedent demand pulls in a paired product |
| 3 | Within-bundle definitional rules (e.g. bed ↔ mattress) | 🟡 sanity check | Catalog plumbing, not behavior. Filtered in chapter 01's triaged rules table |
| 4 | Items in BCG=4 ∩ RFM=4 (low-share, low-growth, old recency) | 🔴 data-quality / catalog hygiene | Caught upstream by the catalog audit (chapter 00). End-of-life — operational decision, not analytical insight |
| 5 | BCG and RFM agreement on top-tier products | 🟢 actionable insight | Robustness signal — investment focus is unambiguous |
| 6 | Recovery of known temporal trends (synthetic-data only) | 🟡 sanity check | Confirms the methods work on engineered signal; not a Real-data finding |
| 7 | Steep CLV concentration (top decile carries the largest share of forecast revenue) | 🟢 actionable insight | Concrete sizing for retention budget allocation |
| 8 | Win-back zone with P(alive) ∈ [0.4, 0.7] and ≥ 1 prior purchase | 🟢 actionable insight | Specific addressable customer list — sized in the *sizing* block |
| 9 | Survival curve flattens between months 6 and 12 | 🟢 actionable insight | Concrete operational thresholds for retention triggers |
| 10 | First-basket features don't predict comeback timing | 🟢 actionable insight (negative result) | Tells us where the targeting signal *isn't*; redirects effort elsewhere |
| 11 | Naive baselines competitive with fancy forecasting | 🟢 actionable insight (methodological) | Don't deploy SARIMA when seasonal-naive is just as good |
| 12 | Embeddings recover catalog category structure | 🟡 sanity check | Confirms basket co-occurrence captures product semantics |
| 13 | Embedding-based substitution table at SKU level | 🟢 actionable insight | Deployable artifact for OOS UI |
| 14 | Discount spend with zero detectable repeat-purchase lift | 🟢 actionable insight | Direct euro amount up for grabs (or A/B-test for targeting heterogeneity); size in the *sizing* block |
**Recommendations below are derived only from the 🟢 rows.** The 🟡 rows confirm the analysis is sound; the 🔴 rows belong in the data-engineering backlog, not the marketing roadmap.
## Headline findings — products & customers
The exact numbers behind each finding are in the *sizing* block above; the prose below explains *why each finding matters*.
1. **Revenue is heavily concentrated in a small head of the catalog.** A short list of products covers a large share of total revenue (see *PRODUCT SIDE → Top 10 products* in the sizing output). The long tail fights over a much smaller pool. Whatever you do with the head moves the P&L; whatever you do with the tail is rounding error.
2. **Co-purchase patterns are strong and exploitable** — once filtered to the *actionable* set in chapter 01. Triage removes definitional within-bundle pairs (which encode catalog structure, not customer choice) and accessory→primary plumbing. What's left are asymmetric cross-bundle rules of the form *anchor → companion*. The top single rule's miss alone (baskets where the antecedent appeared without the consequent) is sized in the sizing block at the average consequent price — concrete EUR figure for a single rule.
3. **BCG and RFM agree on the tiers.** Products in the top BCG cluster (high share + healthy growth) overlap heavily with the top RFM cluster (recent + frequent + valuable). Two different lenses, same conclusion: there is a structurally healthy core and a longer tail.
4. **The delisting candidate list is small.** The BCG=4 ∩ RFM=4 overlap (low share, low growth, old recency) is sized in the sizing block. On synthetic data this picks up the engineered declining items; on real data it surfaces genuine end-of-life SKUs. Either way: tiny revenue contribution, easy delisting decision, frees inventory and catalog space.
5. **Method-vs-data check** *(synthetic only)*: engineered "growing" articles land in the BCG growth quadrant, engineered "declining" articles end up with old recency in RFM. On real data this finding doesn't apply — there's nothing pre-engineered to recover.
6. **Customer value is more skewed than product value.** Top decile of returning customers carries a disproportionate share of 12-month forecast CLV (sized in the sizing block). A flat per-customer marketing budget is a poor allocation rule given this distribution.
7. **The "still alive but quiet" win-back zone is sized in the sizing block.** Customers with `P(alive) ∈ [0.4, 0.7]` and at least one prior purchase. These are furthest from organic re-engagement but not yet write-offs — highest-leverage segment for a single targeted touch.
## Headline findings — time and causality
8. **The comeback rate flattens between months 6 and 12.** Survival on time-to-second-purchase shows the largest drops in the first three months and a gradual flattening from month 6 onward. After roughly day 365 the curve is essentially flat — anyone still silent at year-end is gone for accounting purposes.
9. **First-basket characteristics don't predict comeback timing on this data.** Cox PH on basket value, item count, discount usage, and dominant department — confidence intervals on every hazard ratio cross 1. Real retention drivers live elsewhere (cohort, channel, lifecycle stage) and require those features in the pipeline. With our data we can score *when*, not *who*.
10. **Naive baselines are competitive with fancy forecasting models on a 24-month series.** Seasonal-naive often beats SARIMA / ETS; naive (last value) sometimes beats both. The "boring baselines win" finding is not a bug in our pipeline — it's the M-competition finding repeated for 40 years. With ~24 months of data, *don't buy methodological complexity you can't pay for in observations*.
11. **Embeddings recover the catalog category structure without seeing the labels.** PPMI × SVD on basket co-occurrence places functionally similar SKUs near each other; t-SNE clusters match `product_group` despite the embedding never seeing those labels. The substitution table is the deployable artifact: per SKU, top-3 nearest neighbors as out-of-stock alternatives.
12. **The discount program produces zero detectable lift on repeat-purchase rate.** Discount spend totals are in the sizing block; the naive ATE 95 % CI straddles zero, and both meta-learners (T, S) agree. *On synthetic data with random discount assignment, the null is the truth*; on real data with targeted discounts the same null could mask heterogeneous effects under confounding — but the random-assignment evidence here at least rules out a large average effect.
## Recommendations
Each recommendation below is structured the same way: **what to do**, **on whom (with size)**, **expected impact**, **metric to track**.
### Product side
| Action | Target | Expected impact | Track |
|---|---|---|---|
| **Force a companion-product prompt at the antecedent's page-view / POS scan**, for the strongest *asymmetric* co-purchase pairs from chapter 01's triaged rules table. | Top-N triaged pairs (count and per-pair coverage in the *sizing* block) | Recoverable revenue from the *misses* — baskets where the antecedent appeared without the consequent — sized in the sizing block at the average consequent price | conversion rate of antecedent baskets that include the consequent |
| **Concentrate prime placement on the BCG=1 ∩ RFM=1 overlap** (products both lenses class as healthy core). | The handful of products in that overlap (listed in the sizing block) | Reallocating shelf / landing-page space *to* the already-performing head, *away* from the long tail | revenue share of top-N products over time |
| **Delist BCG=4 ∩ RFM=4 candidates.** Both methods independently flag them as low-share, low-growth, old recency. | The BCG=4 ∩ RFM=4 set (count and revenue contribution in the sizing block) | Frees inventory + catalog space at minimal revenue cost. Replace with adjacent SKUs informed by chapter 08's substitution table | revenue impact ≤ -1 % over the next 6 months (anything more = wrong call) |
| **Wire the chapter 08 embedding similarity table into the "out of stock" UI**: when an SKU is OOS, surface its top-3 cosine neighbors instead of generic "more from category". | Every OOS event on website / POS | Recovered conversions on stockouts; eliminates "we don't have what you want" dead-ends | OOS-bounce rate, OOS-conversion rate |
| **Use seasonal-naive baselines for category inventory planning** with explicit prediction intervals — not point forecasts from SARIMA / ETS. | All product groups, monthly | Honest uncertainty bands prevent overstocking on a single forecast number; on ~24 months SARIMA is no better than naive | next-period MAE per category, year-over-year |
### Customer side
| Action | Target | Expected impact | Track |
|---|---|---|---|
| **Tier marketing budget by 12-month CLV decile.** Top decile gets personal-touch budget; bottom half gets cheap automation. | All returning customers (count and decile contributions in the sizing block) | If current allocation is flat per customer, a CLV-weighted distribution captures the same retention rate at lower cost — or higher retention at the same cost | retention rate × CLV decile, before / after |
| **Win-back trigger sequence at 90 / 180 / 365 days post-last-purchase.** No automation before day 90. Soft email at day 90. Targeted offer at day 180. Reactivation campaign at day 365. After that: stop. | The win-back zone — `P(alive) ∈ [0.4, 0.7]` and ≥ 1 prior purchase (size in the sizing block, refresh weekly) | The survival curve says a meaningful fraction of first-time buyers will eventually return; right-timed prompts convert a fraction of the boundary cases who would otherwise drift to zero | second-purchase rate by trigger window; cost per re-activated customer |
| **Personalize cross-sell email with embedding-based product suggestions** rather than generic "you may also like". The chapter 08 substitution table feeds a per-customer recommendation. | Every active customer, on each post-purchase touch | Higher email click-through; more revenue per email send | post-email basket rate, EUR per recipient |
| **Stop the random-discount program until you can A/B-test it.** Discount spend with zero measurable lift (totals in the sizing block). | All discount-eligible touchpoints | If a real RCT shows the same null in production, that's the full discount spend back as freed budget. If it shows positive lift in a specific segment, target only that segment. The current "spray everyone" policy is indefensible | A/B-tested ATE on repeat-purchase rate at the customer level |
| **Always pair causal claims about marketing with the assumption ledger.** Random assignment + meta-learners is enough; observational data needs propensity weighting / DR-learner / Double ML, or an honest "we cannot conclude". | Any future "did campaign X work?" analysis | Decisions made on biased estimates are worse than no decisions | n/a — process change |
### Quick-win prioritization
If forced to pick a single 90-day initiative ordered by impact-to-effort:
1. **Force the companion-product prompt** on the strongest asymmetric co-purchase pairs. Days, not weeks of work; sizing block quantifies the recoverable miss revenue per rule.
2. **Audit the discount program** with a real A/B test or a proper observational analysis. The full discount spend (sizing block) deserves an answer better than "the model says we don't know".
3. **CLV-weighted marketing tiering** — replaces a flat allocation rule with an evidence-based one; immediate fewer-wasted-EUR effect.
4. **Wire the embedding substitution table** for OOS recovery. Easy engineering, hard-to-measure but consistent uplift.
5. **Delist the BCG=4 ∩ RFM=4 set**. Trivial decision, almost no downside.
## What this chapter is *not*
It is a synthesis, not a model. The numbers above all come from the upstream chapters' methods; this is how a stakeholder report consumes them. For "what should I do tomorrow morning", this is the right level. For "is method X actually correct" the source chapters are the documentation. EUR estimates are first-order back-of-envelope sizings — directionally right, not budget-grade.
